The present disclosure relates generally to systems and methods for magnetic resonance imaging (“MRI”) and, in particular, to systems and methods for mapping cardiac fiber architecture using diffusion-weighted imaging techniques, such as diffusion tensor imaging.
For diffusion MRI techniques, motion sensitizing magnetic field gradients are applied using diffusion weighted imaging (“DWI”) pulse sequences so that the magnetic resonance images include contrast related to the diffusion of water or other fluid molecules. Since microscopic arrangements of tissues often constrain diffusion such that fluid mobility may not be the same in all directions, applying diffusion gradients in several selected directions during the MRI measurement cycle allows diffusion weighted images to be acquired, from which diffusion properties, or coefficients, may be obtained. In the brain, for example, water molecules diffuse more readily along directions of axonal fiber bundles as compared with directions partially or totally orthogonal to the fibers. Hence, the directionality and anisotropy of the apparent diffusion coefficients tend to correlate with the direction of the axonal fibers and fiber bundles. Similarly, in the heart, water diffuses preferentially along myofibers, and so diffusion-encoded imaging techniques allow fiber orientation to be resolved. Hence, application of various processing methods to the diffusion data, allows fibers or fiber bundles to be tracked or segmented, providing indications of normal, injured or diseased tissue construction.
Specifically, in the case of diffusion tensor imaging (“DTI”), three-dimensional distributions of fluid mobility may be represented via tensor field formalism. In order to obtain the apparent diffusion tensor coefficients describing the diffusion tensor, it is generally necessary to acquire at least six DWI images using motion-sensitizing gradients directed in six different directions. Indeed, it may be desirable to acquire more than six directions, but the acquisition of additional DWI images may extend the total scan time. As is known in the art, a diffusion tensor for each voxel provides a reference frame, or eigensystem, that includes orthogonal axes termed eigenvectors, êi, whereby eigenvalues, λi, along the eigenvectors correspond to the degree of diffusivity along each of the major axes of the diffusion tensor. Typically, the orientation of the tensor is commonly taken to be parallel to the principal eigenvector, ê1, describing the direction of largest diffusion, or the eigenvector associated with the largest eigenvalue, λ1. For anisotropic fluid diffusion, as observed along tissue fibers or fiber bundles, the principal eigenvector is generally assumed to be collinear with the dominant fiber or fiber bundle orientation.
In particular, heart wall myofibers have been shown to wind as helices around the ventricle chambers, having been resolved by way of histological investigations using sectioned samples, as well as using non-invasive imaging, such as DTI techniques. Presenting additional complication, in vivo data has shown that myofiber architecture is dynamic, as in the case when the left ventricle (“LV”) contracts and relaxes. Microstructural changes in tissues, like the myocardium, are commonly quantified by measuring invariants of the tensors, such as mean diffusivity (“MD”), fractional anisotropy (“FA”), or mode for each location, or voxel, in a region of interest. These invariants provide a basis for comparing tensor components between different tissues or regions. Specifically, the MD describes an average diffusivity, while the FA measures the magnitude of the anisotropic component of the tensor, and the mode describes the type of anisotropy, such as planar anisotropic, orthotropic, or linear anisotropic.
These indices have been widely used in ex vivo cardiac DTI studies of both healthy and diseased myocardium, and have been used in humans in vivo to characterize the microstructural integrity of the myocardium after infarction. Most architecture-related information derived from the DTI data has relied solely upon the diffusion along the principal eigenvector. For example, the helix angle (“HA”) metric relies upon the orientation, or inclination, of the principal eigenvector, while a more recent approach quantifies a propagation angle (PA) that measures the angle between two adjacent principal eigenvectors relative to a given myofiber. However, the ability of these metrics to fully characterize structural dynamics during heart activity is limited, and their reproducibility in the human heart in vivo is unknown.
Therefore, given the above, there is a need for systems and methods directed to improved myocardial tissue architecture mapping.